Today, finance is one the most significant industries in the world. Management of this sector always used to require a lot of time and effort. However, it started to change. Application of data science into finance accelerated analytics processes and facilitated the decision-making process.
The main purposes of using data science solutions in finance do not differ from the reasons which are present in other industries. Data science in the financial industry is used for security purposes, risk analytics, customer management, fraud detection, and many more reasons. Let’s take a closer look at some of these applications.
Every company faces some kind of risk before making crucial business decisions. The process of analyzing possible threats that can be the result of such a decision is called risk analytics. Since decision making is vital in finance companies and institutions, data science turns out to be especially useful in these processes.
Risks that endanger companies’ well-being can have various sources. They can come from competitors, market, credit, internal issues, etc. In order to manage the risk, the threat needs to be identified. Once it is done, threats should be monitored and prioritized. Customer information and data derived from financial transactions can be used to create a scoring model and optimize operating costs. This aspect of risk analysis is essential to estimate the customer’s creditworthiness. Prediction of risk and challenges that a given customer can impose is usually made with the use of machine learning algorithms that analyze and find patterns in past transactions made by a customer.
In the past, data was processed and analyzed in batches. Therefore, a real-time analysis was out of the question. By the time data was analyzed, it had already become old and outdated. At that time, the decisions based on historical data have been inaccurate and frequently wrong.
The ability to gain information from real-time data is a huge advantage for financial companies. Financial institutions can keep track of all transactions, credit scores, or financial attributions without the fear of using data that is out of date.
Consumer data management
The day-to-day financial institutions’ functioning went through a complete revolution once big data was introduced to their work. Such institutions need to deal with huge data volumes that need to be classified and analyzed in real time. Insights about customers and their behaviors gained from such data help predict changes and trends that are likely to occur in the financial market and its values.
Personalized services are key to success in building good, long-lasting relationships with customers and increasing selling by creating an offer tailored to a given person. The creation of such offers is possible thanks to AI tools, which derive the needed information from personal data.
Other solutions that are useful in improving communication with customers are natural language processing (NLP) and speech recognition. Banking institutions often create AI-powered customer lines, which enable solving issues of the customers without the interference of human consultants. Thanks to this solution, the customer does not need to waste time waiting for their turn on the traditional customer hotline.
Fraud detection and security
Frauds are one of the significant threats for financial institutions—chances of fraud increase with a growing number of transactions. Financial institutions constantly develop tools and algorithms which help them detect possible frauds. In the event of any anomalies on customer accounts, institutions are informed about them in real time. Thanks to that approach, all unusual transaction patterns can be taken for further investigation.
Data that is owned by financial institutions is highly sensitive. That is why access to it should be properly protected. Accounts of customers and institution employees should be secured with few factor authentication, which can include face and voice recognition or other AI-powered tools.
Consumer personalization is one of the major operations performed by financial institutions. Data science can provide companies with insights to customer behavior analytics in real time. Thanks to that, employees and systems are able to make more efficient business decisions. This data science application is particularly useful in insurance and banking companies.
Data science is a very significant component of the finance industry. It leads to better analysis and decision making which results in higher profits for financial institutions. Companies are also to analyze trends and patterns in data, which helps risk and scam reduction and minimize losses. If a financial company wants to achieve success on the market, it cannot do without data science-related solutions. Since the incorporation of this technology into company structures can be challenging, we offer complex AI consulting services.
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